Hyposmia correlates with axial signs and gait disorder in Parkinson's disease: an Italian Olfactory Identification Test study.

BACKGROUND: Olfactory dysfunction is a non-motor symptom and an important biomarker of Parkinson's disease (PD) because of its high prevalence (> 90%). Whether hyposmia correlates with motor symptoms is unclear. In the present study, we aim to investigate the relationship between olfactory impairment with both motor and non-motor features and disease variables (disease duration, stage, and severity). METHODS: One-hundred fifty-four PD patients were evaluated. Odor identification ability was tested using Italian Olfactory Identification Test (IOIT). A comprehensive spectrum of motor and non-motor features was assessed. Cognitive function was investigated through MMSE. Patients were divided into 3 different clinical phenotypes using UPDRS-III: tremor-dominant type (TDT), akinetic-rigid type (ART), and mixed type (MXT). RESULTS: Three of the 33 IOIT items were most frequently misidentified: basil (74.3%), coffee (66.9%), and mushroom (59.6%). Hyposmia was found in 93%. Hyposmic patients were older than controls (p = 0.01). Hoehn & Yahr (H&Y) score of 2 or greater was associated with higher probability of being hyposmic (OR = 5.2, p = 0.01). IOIT score did not significantly differ between TDT, ART, and MXT of analyzed PD patients. Performance to IOIT inversely correlated with age (p < 0.01), disease duration (p = 0.01), and H&Y score of 2 or higher (p < 0.01). Clinical features that associated with higher IOIT score were freezing of gait (FOG) (p < 0.001) and camptocormia (p < 0.05). CONCLUSIONS: In our cohort, IOIT scores showed a positive correlation with axial motor signs, but not with non-motor symptoms. IOIT may be a useful tool not only for supporting PD diagnosis but also for providing prognostic information about motor function.

A Comprehensive Review of COVID-19-Related Olfactory Deficiency: Unraveling Associations with Neurocognitive Disorders and Magnetic Resonance Imaging Findings.

Olfactory dysfunction (OD) is one of the most common symptoms in COVID-19 patients and can impact patients' lives significantly. The aim of this review was to investigate the multifaceted impact of COVID-19 on the olfactory system and to provide an overview of magnetic resonance (MRI) findings and neurocognitive disorders in patients with COVID-19-related OD. Extensive searches were conducted across PubMed, Scopus, and Google Scholar until 5 December 2023. The included articles were 12 observational studies and 1 case report that assess structural changes in olfactory structures, highlighted through MRI, and 10 studies correlating the loss of smell with neurocognitive disorders or mood disorders in COVID-19 patients. MRI findings consistently indicate volumetric abnormalities, altered signal intensity of olfactory bulbs (OBs), and anomalies in the olfactory cortex among COVID-19 patients with persistent OD. The correlation between OD and neurocognitive deficits reveals associations with cognitive impairment, memory deficits, and persistent depressive symptoms. Treatment approaches, including olfactory training and pharmacological interventions, are discussed, emphasizing the need for sustained therapeutic interventions. This review points out several limitations in the current literature while exploring the intricate effects of COVID-19 on OD and its connection to cognitive deficits and mood disorders. The lack of objective olfactory measurements in some studies and potential validity issues in self-reports emphasize the need for cautious interpretation. Our research highlights the critical need for extensive studies with larger samples, proper controls, and objective measurements to deepen our understanding of COVID-19's long-term effects on neurological and olfactory dysfunctions.

Hands-feet wireless devices: Test-retest reliability and discriminant validity of motor measures in Parkinson's disease telemonitoring.

BACKGROUND: Telemonitoring, a branch of telemedicine, involves the use of technological tools to remotely detect clinical data and evaluate patients. Telemonitoring of patients with Parkinson's disease (PD) should be performed using reliable and discriminant motor measures. Furthermore, the method of data collection and transmission, and the type of subjects suitable for telemonitoring must be well defined. OBJECTIVE: To analyze differences in patients with PD and healthy controls (HC) with the wearable inertial device SensHands-SensFeet (SH-SF), adopting a standardized acquisition mode, to verify if motor measures provided by SH-SF have a high discriminating capacity and high intraclass correlation coefficient (ICC). METHODS: Altogether, 64 patients with mild-to-moderate PD and 50 HC performed 14 standardized motor activities for assessing bradykinesia, postural and resting tremors, and gait parameters. SH-SF inertial devices were used to acquire movements and calculate objective motor measures of movement (total: 75). For each motor task, five or more biomechanical parameters were measured twice. The results were compared between patients with PD and HC. RESULTS: Fifty-eight objective motor measures significantly differed between patients with PD and HC; among these, 32 demonstrated relevant discrimination power (Cohen's d > 0.8). The test-retest reliability was excellent in patients with PD (median ICC = 0.85 right limbs, 0.91 left limbs) and HC (median ICC = 0.78 right limbs, 0.82 left limbs). CONCLUSION: In a supervised environment, the SH-SF device provides motor measures with good results in terms of reliability and discriminant ability. The reliability of SH-SF measurements should be evaluated in an unsupervised home setting in future studies.

Testing olfactory dysfunction in acute and recovered COVID-19 patients: a single center study in Italy.

BACKGROUND: Olfactory dysfunction in coronavirus disease 2019 (COVID-19) is common during acute illness and appears to last longer than other symptoms. The aim of this study was to objectively investigate olfactory dysfunction in two cohorts of patients at two different stages: during acute illness and after a median recovery of 4 months. METHODS: Twenty-five acutely ill patients and 26 recovered subjects were investigated. Acute patients had a molecular diagnosis of COVID-19; recovered subjects had a positive antibody assay and a negative molecular test. A 33-item psychophysical olfactory identification test tailored for the Italian population was performed. RESULTS: Median time from symptoms onset to olfactory test was 33 days in acute patients and 122 days in recovered subjects. The former scored a significantly higher number of errors at psychophysical testing (median [IQR]: 8 [13] vs 3 [2], p < 0.001) and were more frequently hyposmic (64% vs 19%, p = 0.002). Recovered subjects reported a variable time to subjective olfactory recovery, from days up to 4 months. Participants included in the study reported no significant nasal symptoms at olfactory testing. Among recovered subject who reported olfactory loss during acute COVID-19, four (27%) were still hyposmic. Demographic and clinical characteristics did not show significant associations with olfactory dysfunction. CONCLUSION: Moderate-to-severe hospitalized patients showed a high level and frequency of olfactory dysfunction compared to recovered subjects. In the latter group, subjects who reported persisting olfactory dysfunction showed abnormal scores on psychophysical testing, indicating that, at least in some subjects, persistent hyposmia may represent a long-term sequela of COVID-19.

Data-Driven Models for Objective Grading Improvement of Parkinson's Disease.

Parkinson's disease (PD) is a progressive disorder of the central nervous system that causes motor dysfunctions in affected patients. Objective assessment of symptoms can support neurologists in fine evaluations, improving patients' quality of care. Herein, this study aimed to develop data-driven models based on regression algorithms to investigate the potential of kinematic features to predict PD severity levels. Sixty-four patients with PD (PwPD) and 50 healthy subjects of control (HC) were asked to perform 13 motor tasks from the MDS-UPDRS III while wearing wearable inertial sensors. Simultaneously, the clinician provided the evaluation of the tasks based on the MDS-UPDRS scores. One hundred-ninety kinematic features were extracted from the inertial motor data. Data processing and statistical analysis identified a set of parameters able to distinguish between HC and PwPD. Then, multiple feature selection methods allowed selecting the best subset of parameters for obtaining the greatest accuracy when used as input for several predicting regression algorithms. The maximum correlation coefficient, equal to 0.814, was obtained with the adaptive neuro-fuzzy inference system (ANFIS). Therefore, this predictive model could be useful as a decision support system for a reliable objective assessment of PD severity levels based on motion performance, improving patients monitoring over time.

A Wearable System to Objectify Assessment of Motor Tasks for Supporting Parkinson's Disease Diagnosis.

Objective assessment of the motor evaluation test for Parkinson's disease (PD) diagnosis is an open issue both for clinical and technical experts since it could improve current clinical practice with benefits both for patients and healthcare systems. In this work, a wearable system composed of four inertial devices (two SensHand and two SensFoot), and related processing algorithms for extracting parameters from limbs motion was tested on 40 healthy subjects and 40 PD patients. Seventy-eight and 96 kinematic parameters were measured from lower and upper limbs, respectively. Statistical and correlation analysis allowed to define four datasets that were used to train and test five supervised learning classifiers. Excellent discrimination between the two groups was obtained with all the classifiers (average accuracy ranging from 0.936 to 0.960) and all the datasets (average accuracy ranging from 0.953 to 0.966), over three conditions that included parameters derived from lower, upper or all limbs. The best performances (accuracy = 1.00) were obtained when classifying all the limbs with linear support vector machine (SVM) or gaussian SVM. Even if further studies should be done, the current results are strongly promising to improve this system as a support tool for clinicians in objectifying PD diagnosis and monitoring.

Objective assessment of blinking and facial expressions in Parkinson's disease using a vertical electro-oculogram and facial surface electromyography.

OBJECTIVE: Hypomimia is a common and early symptom of Parkinson's disease (PD), which reduces the ability of PD patients to manifest emotions. Currently, it is visually evaluated by the neurologist during neurological examinations for PD diagnosis, as described in task 3.2 of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Since such an evaluation is semi-quantitative and affected by inter-variability, this paper aims to measure the physiological parameters related to eye blink and facial expressions extracted from a vertical electro-oculogram (VEOG) and facial surface electromyography (fsEMG) to differentiate PD patients from healthy control subjects (HCs). APPROACH: The spontaneous eye blink rate-minute (sEBR), its maximum amplitude (BMP), and facial cutaneous muscle activity were measured in 24 PD patients and 24 HCs while the subjects looked at a visual-tester composed of three main parts: static vision, dynamic vision and reading silently. Specificity and sensitivity for each parameter were calculated. MAIN RESULTS: The VEOG and the fsEMG allowed the identification of some parameters related to eye blink and facial expressions (i.e. sEBR, BMP, frontal and peribuccal muscular activities), being able to distinguish between PD patients and HCs with high sensitivity and specificity. SIGNIFICANCE: The demonstration that the combination of parameters related to eye blink and facial expressions can discriminate (with high accuracy) between PD patients versus HCs, thus resulting in a useful tool to support the neurologist in objective assessment of hypomimia for improving PD diagnosis.

Upper limb motor pre-clinical assessment in Parkinson's disease using machine learning.

INTRODUCTION: Parkinson's disease (PD) is a common neurodegenerative disorder characterized by disabling motor and non-motor symptoms. For example, idiopathic hyposmia (IH), which is a reduced olfactory sensitivity, is typical in >95% of PD patients and is a preclinical marker for the pathology. METHODS: In this work, a wearable inertial device, named SensHand V1, was used to acquire motion data from the upper limbs during the performance of six tasks selected by MDS-UPDRS III. Three groups of people were enrolled, including 30 healthy subjects, 30 IH people, and 30 PD patients. Forty-eight parameters per side were computed by spatiotemporal and frequency data analysis. A feature array was selected as the most significant to discriminate among the different classes both in two-group and three-group classification. Multiple analyses were performed comparing three supervised learning algorithms, Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes, on three different datasets. RESULTS: Excellent results were obtained for healthy vs. patients classification (F-Measure 0.95 for RF and 0.97 for SVM), and good results were achieved by including subjects with hyposmia as a separate group (0.79 accuracy, 0.80 precision with RF) within a three-group classification. Overall, RF classifiers were the best approach for this application. CONCLUSION: The system is suitable to support an objective PD diagnosis. Further, combining motion analysis with a validated olfactory screening test, a two-step non-invasive, low-cost procedure can be defined to appropriately analyze people at risk for PD development, helping clinicians to identify also subtle changes in motor performance that characterize PD onset.

Automated Systems Based on Wearable Sensors for the Management of Parkinson's Disease at Home: A Systematic Review.

BACKGROUND: Parkinson's disease is a common neurodegenerative pathology that significantly influences quality of life (QoL) of people affected. The increasing interest and development in telemedicine services and internet of things technologies aim to implement automated smart systems for remote assistance of patients. The wide variability of Parkinson's disease in the clinical expression, as well as in the symptom progression, seems to address the patients' care toward a personalized therapy. OBJECTIVES: This review addresses automated systems based on wearable/portable devices for the remote treatment and management of Parkinson's disease. The idea is to obtain an overview of the telehealth and automated systems currently developed to address the impairments due to the pathology to allow clinicians to improve the quality of care for Parkinson's disease with benefits for patients in QoL. DATA SOURCES: The research was conducted within three databases: IEEE Xplore®, Web of Science®, and PubMed Central®, between January 2008 and September 2017. STUDY ELIGIBILITY CRITERIA: Accurate exclusion criteria and selection strategy were applied to screen the 173 articles found. RESULTS: Ultimately, 55 articles were fully evaluated and included in this review. Divided into three categories, they were automated systems actually tested at home, implemented mobile applications for Parkinson's disease assessment, or described a telehealth system architecture. CONCLUSION: This review would provide an exhaustive overview of wearable systems for the remote management and automated assessment of Parkinson's disease, taking into account the reliability and acceptability of the implemented technologies.

Comparative Motor Pre-clinical Assessment in Parkinson's Disease Using Supervised Machine Learning Approaches.

Millions of people worldwide are affected by Parkinson's disease (PD), which significantly worsens their quality of life. Currently, the diagnosis is based on assessment of motor symptoms, but interest toward non-motor symptoms is increasing, as well. Among them, idiopathic hyposmia (IH) is associated with an increased risk of developing PD in healthy adults. In this work, a wearable inertial device, named SensFoot V2, was used to acquire motor data from 30 healthy subjects, 30 people with IH, and 30 PD patients while performing tasks from the MDS-UPDRS III for lower limb assessment. The most significant and non-correlated extracted parameters were selected in a feature array that can identify differences between the three groups of people. A comparative classification analysis was performed by applying three supervised machine learning algorithms. The system resulted able to distinguish between healthy and patients (specificity and recall equal to 0.967), and the people with IH can be identified as a separate class within a three-group classification (accuracy equal to 0.78). Thus, the system could support the clinician in objective assessment of PD. Further, identification of IH together with changes in motor parameters could be a non-invasive two-step approach to investigate the early onset of PD.

How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review.

Background: Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology. OBJECTIVES: This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring). DATA SOURCES: The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016. STUDY ELIGIBILITY CRITERIA: Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers. RESULTS: Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.

Preliminary evaluation of SensHand V1 in assessing motor skills performance in Parkinson disease.

Nowadays, the increasing old population 65+ as well as the pace imposed by work activities lead to a high number of people that have particular injuries for limbs. In addition to persistent or temporary disabilities related to accidental injuries we must take into account that part of the population suffers from motor deficits of the hands due to stroke or diseases of various clinical nature. The most recurrent technological solutions to measure the rehabilitation or skill motor performance of the hand are glove-based devices, able to faithfully capture the movements of the hand and fingers. This paper presents a system for hand motion analysis based on 9-axis complete inertial modules and dedicated microcontroller which are fixed on fingers and forearm. The technological solution presented is able to track the patients' hand motions in real-time and then to send data through wireless communication reducing the clutter and the disadvantages of a glove equipped with sensors through a different technological structure. The device proposed has been tested in the study of Parkinson's disease.

The validity and reliability of the Italian Olfactory Identification Test (IOIT) in healthy subjects and in Parkinson's disease patients.

BACKGROUND: Olfactory function can be rapidly evaluated by means of standardized olfactory tests. Multiple-choice smell identification tests can be conditioned by cultural background. To investigate a new tool for detecting olfactory deficit in Italian subjects we developed a multiple-choice identification test prepared with odorants belonging to the Italian culture. METHODS: The Italian Olfactory Identification Test (IOIT) was developed with 33 microencapsulated odorants with intensity of odors and headspace Gas Chromatography being tested. Test-retest reliability of the IOIT was evaluated. The IOIT was administered to 511 controls and 133 Parkinson's patients. RESULTS: In healthy subjects the number of IOIT errors increased with age for both females (p < 0.0001) and males (p < 0.0001), while in the Parkinson's disease group the number of IOIT errors was significantly greater where compared to healthy subjects (p < 0.0001 in all age groups). The reference limits applied to all age groups revealed an IOIT sensitivity of 93% and a specificity of 99%. The test-retest reliability was excellent. CONCLUSION: The IOIT is highly reliable, disposable, easy to administer, not fragile, and has a long shelf-life. All these features make the IOIT suitable for clinical use as well as for population screening and to discriminate Parkinson's patients from healthy subjects.